Current and Emerging 3D Models to Study Breast Cancer

Roberts, Sophie; Peyman, Sally A.; Speirs, Valerie

doi:10.1007/978-3-030-20301-6_22

Cited by 21 publications

(13 citation statements)

References 59 publications

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“…Adaption of newer technologies, such as microfluidic tumor-or organ-on-a-chip systems and 3D bioprinting, present means by which researchers can perform in vitro and ex vivo work in settings that are more comparable with the in vivo system. In particular, microfluidic platforms have revealed insight into metastatic cascade, cell migration, transport of anticancer agents, and even in predictive application for determination of tumor response to cancer treatments (Belgodere et al, 2018;Roberts et al, 2019;Sun et al, 2019;Trujillo-de Santiago et al, 2019). 3D bioprinting allows for fine control over the recreation of the tumor microenvironment (including the structural and compositional heterogeneity [Belgodere et al, 2018;Heinrich et al, 2019;Langer et al, 2019;Ma et al, 2018]) resulting in in vitro models that are realistic representations of an in vivo tumor.…”

Section: Limitations Of Using In Vitro and Ex Vivo Data For Modeling Cancermentioning

confidence: 99%

Integrating Quantitative Assays with Biologically Based Mathematical Modeling for Predictive Oncology

et al. 2020

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Summary We provide an overview on the use of biological assays to calibrate and initialize mechanism-based models of cancer phenomena. Although artificial intelligence methods currently dominate the landscape in computational oncology, mathematical models that seek to explicitly incorporate biological mechanisms into their formalism are of increasing interest. These models can guide experimental design and provide insights into the underlying mechanisms of cancer progression. Historically, these models have included a myriad of parameters that have been difficult to quantify in biologically relevant systems, limiting their practical insights. Recently, however, there has been much interest calibrating biologically based models with the quantitative measurements available from (for example) RNA sequencing, time-resolved microscopy, and in vivo imaging. In this contribution, we summarize how a variety of experimental methods quantify tumor characteristics from the molecular to tissue scales and describe how such data can be directly integrated with mechanism-based models to improve predictions of tumor growth and treatment response.

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Section: Limitations Of Using In Vitro and Ex Vivo Data For Modeling Cancermentioning

confidence: 99%

Integrating Quantitative Assays with Biologically Based Mathematical Modeling for Predictive Oncology

et al. 2020

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“…Tissue slice models consist of thin slices of tissues collected from patients through biopsies, retaining their native state and preserving cell-cell and cell-ECM connections [79]. These models are very delicate to maintain and are subjected to higher ethical concerns once they use patient samples.…”

Section: Tissue Slice Modelsmentioning

confidence: 99%

“…The preservation of the interactions between cells and cells and ECM makes the diffusion of the medium through the tissues possible [79], allowing the application of these models to study drug response [80] and the use of adenoviral vector strategies to develop novel gene therapies and virotherapies [81][82][83].…”

Section: Tissue Slice Modelsmentioning

confidence: 99%

“…Organoids are 3D assemblies of cells of one or more types with histologic arrangements at the micron scale similar to in vivo tumors [79,84]. More commonly, organoids are composed of malignant cells and supporting cells from the tumor microenvironment, such as fibroblasts, leucocytes, and endothelial cells, among others.…”

Section: Organoidsmentioning

confidence: 99%

“…Three-dimensional 3D spheroid models include sophisticated structures composed by one or more types of cells which, when grown enough, present a necrotic core surrounded by actively proliferating layers of cells [79,88]. The complex formed also includes an extracellular matrix (ECM) formed of proteins produced by the cells.…”

Section: Spheroidsmentioning

confidence: 99%

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Experimental Models as Refined Translational Tools for Breast Cancer Research

et al. 2020

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Breast cancer is one of the most common cancers worldwide, which makes it a very impactful malignancy in the society. Breast cancers can be classified through different systems based on the main tumor features and gene, protein, and cell receptors expression, which will determine the most advisable therapeutic course and expected outcomes. Multiple therapeutic options have already been proposed and implemented for breast cancer treatment. Nonetheless, their use and efficacy still greatly depend on the tumor classification, and treatments are commonly associated with invasiveness, pain, discomfort, severe side effects, and poor specificity. This has demanded an investment in the research of the mechanisms behind the disease progression, evolution, and associated risk factors, and on novel diagnostic and therapeutic techniques. However, advances in the understanding and assessment of breast cancer are dependent on the ability to mimic the properties and microenvironment of tumors in vivo, which can be achieved through experimentation on animal models. This review covers an overview of the main animal models used in breast cancer research, namely in vitro models, in vivo models, in silico models, and other models. For each model, the main characteristics, advantages, and challenges associated to their use are highlighted.

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Anticancer Activity and Mechanism of Action Evaluation of an Acylhydrazone Cu(II) Complex toward Breast Cancer Cells, Spheroids, and Mammospheres

et al. 2021

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The purpose of this work was to screen the anticancer activity and mechanisms of action of Cu(II)-acylhydrazone complex [Cu(HL)(H 2 O)](NO 3 )•H 2 O, (CuHL), to find a potential novel agent for breast chemotherapies. Cytotoxicity studies on MCF7 cells demonstrated that CuHL has stronger anticancer properties than cisplatin over breast cancer cell models. Computational simulations showed that CuHL could interact in the minor groove of the DNA dodecamer, inducing a significant genotoxic effect on both cancer cells from 0.5 to 1 μM. In this sense, molecular docking and molecular dynamics simulations showed that the compound could interact with 20S proteasome subunits. Also, cell proteasome experiments using breast cancer cells revealed that the complex can inhibit proteasomal activity. Moreover, CuHL induced apoptosis in breast cancer cells at very low micromolar concentrations (0.5-2.5 μM) and displayed relevant anticancer activity over spheroids derived from MCF7 cells. Ultimately, CuHL diminished the number of mammospheres formed, disturbing their morphology and size.

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Current and Emerging 3D Models to Study Breast Cancer

Cited by 21 publications

References 59 publications

Integrating Quantitative Assays with Biologically Based Mathematical Modeling for Predictive Oncology

Integrating Quantitative Assays with Biologically Based Mathematical Modeling for Predictive Oncology

Experimental Models as Refined Translational Tools for Breast Cancer Research

Anticancer Activity and Mechanism of Action Evaluation of an Acylhydrazone Cu(II) Complex toward Breast Cancer Cells, Spheroids, and Mammospheres

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